Paper Info
Title
First Results from Experiments in Fuzzy Classifier System Architectures for Mobile Robotics
Abstract
We present first results from a comparison between a Fuzzy Classifier System operating at the level of whole rule-bases, and three variants of one that operates at the level of individual rules. The application domain is mobile robotics, and the problem is autonomous acquisition of an "investigative" obstacle avoidance competency. The Fuzzy Classifier Systems operate on the rules of fuzzy controllers with pre-defined fuzzy membership functions. Generally, all of the methods used were capable of producing fuzzy controllers with competencies that exceeded that of a simple hand-coded fuzzy controller that we had devised. The approach operating at the level of whole rule-bases yielded more robust and stable convergence on high performance solutions than any other architecture presented here. It is clear from the results that more work needs to be done to unravel the disappointing convergence dynamics of the algorithms operating at the level of individual rules.
Year
DOI
Venue
2002
10.1007/3-540-45712-7_56
PPSN
Keywords
Field
DocType
fuzzy classifier systems,disappointing convergence dynamic,mobile robotics,individual rule,whole rule-bases,fuzzy classifier system operating,simple hand-coded fuzzy controller,fuzzy controller,pre-defined fuzzy membership function,fuzzy classifier system architectures,first results,algorithms operating,approach operating,system architecture,rule based,obstacle avoidance,mobile robot
Fuzzy electronics,Neuro-fuzzy,Fuzzy classification,Defuzzification,Computer science,Fuzzy set operations,Artificial intelligence,Fuzzy number,Membership function,Machine learning,Fuzzy rule
Conference
ISBN
Citations 
PageRank 
3-540-44139-5
4
0.36
References 
Authors
16
2
Name
Order
Citations
PageRank
Anthony G. Pipe125539.08
Brian Carse225926.31